The invention relates generally to methods and systems for removing inherent autofluorescence of biological materials from images of those biological materials.
Tissue auto-fluorescence (AF) is a fundamental problem in microscopy and surgical applications. It reduces the signal detection sensitivity, and in some cases may cause failure in the detection of fluorescent dye signals. Accurate detection of protein-specific fluorescent dyes is critical for many microscopy applications, as such molecular pathology imaging, where quantitation of molecular pathways has significant implications such as predicting drug response, therapy planning, and population segmentation of cancer patients.
In recent years the development of numerous fluorescent dyes has made optical fluorescent microscopy the method of choice for disease recognition. Numerous studies have used fluorescent spectroscopy techniques to study the variations in tissue auto-fluorescence for diagnosis of colorectal, breast, lung, cervical, colon, gastrointestinal tract, and cancer. However, these methods require extensive modeling of tissue-specific auto-fluorescence (AF) spectra. This tedious modeling process, which may not always be sufficient, can be side stepped by using multiplexing techniques in which artificially introduced dyes or dyes are used to track specific proteins. Multiplexing involves acquiring images of different dyes with non-overlapping emission or excitation spectra through filter cubes that match the emission and excitation spectra of each dye. However, in such methods, the protein-specific fluorescence emitted by these dyes, upon appropriate external light excitation, is combined in unknown proportions with the inherent tissue autofluorescence (AF) signal, greatly reducing their efficacy. Thus separation and removal of inherent tissue AF would greatly improve the accuracy of such methods.
Although various strategies for tissue AF removal have been proposed and studied in the literature, such as, using liquid crystal tunable filters, fluorescence polarization, dual-wavelength differential fluorescence correction, confocal laser scanning microscopy and time-resolved fluorescence microscopy, many of these strategies make use of expensive multi-spectral imaging hardware, over the entire spectral range, followed by spectral un-mixing. Apart from hardware augmentation, there are also various chemical processes that can be used to reduce the effect of tissue AF.
Digitally acquired fluorescence microscope images can also be processed retrospectively using software methods, to separate tissue AF from the relevant dye fluorescence. Some of these methods rely on acquiring estimates of the pure AF signal and using them to remove AF from images containing both dye and AF signals by a weighted subtraction. Others use statistical correlation techniques to correct for the additive AF signal. While these techniques are more cost effective than using multi-spectral imaging hardware, they may not be able to completely remove the AF component from fluorescence microscopy images.